research

Multi-LLM synthesis works by decomposing complex queries into sub-questions and processing them in parallel across AI models like Perplexity, Gemini, OpenAI, and Claude. This approach ensures diverse perspectives and robust analysis for comprehensive, well-cited research reports.

Context-aware research adapts investigations based on user-defined domains like travel, work, or shopping. You need it when queries require dynamic data fetching, such as researching current point balances alongside flight options and local customs for international trip planning.

You conduct exhaustive investigation by defining your topic and allowing the system to dynamically fetch data via browser control. It decomposes your query into sub-questions, runs multi-LLM parallel research, and synthesizes findings into cited reports with actionable recommendations.

Yes, you can use multi-LLM research for domain-specific travel planning. It adapts to travel contexts by fetching dynamic data, researching accommodations and loyalty program benefits, and synthesizing all findings into actionable recommendations for complex international trips.

The best way to synthesize findings from multiple AI agents is using a research tool that automates parallel queries across models like Claude and Gemini. It handles domain-specific context loading and generates comprehensive, cited reports with actionable recommendations.

Deep investigation takes hours rather than seconds to ensure no stone is left unturned. This extended processing time accommodates query decomposition, multi-LLM parallel research, dynamic data fetching, and comprehensive synthesis into well-cited reports.